U.S. patent application number 15/672797 was filed with the patent office on 2019-01-03 for scanning electron microscope objective lens calibration.
The applicant listed for this patent is KLA-Tencor Corporation. Invention is credited to Thanh Ha, Ichiro Honjo, Christopher Sears, Jianwei Wang, Huina Xu, Hedong Yang.
Application Number | 20190004298 15/672797 |
Document ID | / |
Family ID | 64734421 |
Filed Date | 2019-01-03 |
United States Patent
Application |
20190004298 |
Kind Code |
A1 |
Honjo; Ichiro ; et
al. |
January 3, 2019 |
Scanning Electron Microscope Objective Lens Calibration
Abstract
Objective lens alignment of a scanning electron microscope
review tool with fewer image acquisitions can be obtained using the
disclosed techniques and systems. Two different X-Y voltage pairs
for the scanning electron microscope can be determined based on
images. A second image based on the first X-Y voltage pair can be
used to determine a second X-Y voltage pair. The X-Y voltage pairs
can be applied at the Q4 lens or other optical components of the
scanning electron microscope.
Inventors: |
Honjo; Ichiro; (Santa Clara,
CA) ; Sears; Christopher; (San Jose, CA) ;
Yang; Hedong; (Santa Clara, CA) ; Ha; Thanh;
(Milpitas, CA) ; Wang; Jianwei; (San Jose, CA)
; Xu; Huina; (San Jose, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
KLA-Tencor Corporation |
Milpitas |
CA |
US |
|
|
Family ID: |
64734421 |
Appl. No.: |
15/672797 |
Filed: |
August 9, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62526804 |
Jun 29, 2017 |
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H01J 37/3007 20130101;
H01J 37/10 20130101; H01L 2924/01079 20130101; H01J 2237/2826
20130101; G02B 21/008 20130101; H01L 2924/0105 20130101; H01J
37/226 20130101; H01J 37/28 20130101; G06N 3/0454 20130101; H01J
37/222 20130101; G06N 3/08 20130101 |
International
Class: |
G02B 21/00 20060101
G02B021/00; H01J 37/22 20060101 H01J037/22; H01J 37/30 20060101
H01J037/30; G06N 3/08 20060101 G06N003/08 |
Claims
1. A method comprising: receiving a first image at a control unit,
wherein the first image provides alignment information of an
objective lens in a scanning electron microscope system;
determining, using the control unit, a first X-Y voltage pair based
on the first image, wherein the first X-Y voltage pair provides
alignment of the objective lens closer to a center of an alignment
target than in the first image; communicating, using the control
unit, the first X-Y voltage pair to the scanning electron
microscope system; receiving a second image at the control unit,
wherein the second image provides alignment information of the
objective lens and the second image is a result of settings of the
first X-Y voltage pair; determining, using the control unit, a
second X-Y voltage pair based on the second image, wherein the
second X-Y voltage pair provides alignment of the objective lens
closer to the center of the alignment target than the first X-Y
voltage pair; and communicating, using the control unit, the second
X-Y voltage pair to the scanning electron microscope system.
2. The method of claim 1, wherein the first X-Y voltage pair is one
class.
3. The method of claim 1, wherein the second X-Y voltage pair is a
continuous value.
4. The method of claim 1, wherein the second X-Y voltage pair is
based on an average of a plurality of results.
5. The method of claim 1, further comprising: applying the first
X-Y voltage pair to a Q4 lens of the scanning electron microscope
before generating the second image; and applying the second X-Y
voltage pair to the Q4 lens of the scanning electron
microscope.
6. The method of claim 1, wherein determining the first X-Y voltage
pair uses a first deep learning neural network.
7. The method of claim 6, wherein the first deep learning neural
network includes a classification network.
8. The method of claim 1, wherein determining the second X-Y
voltage pair uses a second deep learning neural network.
9. The method of claim 8, wherein the second deep learning neural
network includes a regression network ensemble.
10. The method of claim 1, wherein the first image and the second
image are of a carbon substrate with gold-plated tin spheres on a
carbon substrate.
11. A non-transitory computer readable medium storing a program
configured to instruct a processor to: receive a first image,
wherein the first image provides alignment information of an
objective lens in a scanning electron microscope system; determine
a first X-Y voltage pair based on the first image, wherein the
first X-Y voltage pair provides alignment of the objective lens
closer to a center of an alignment target than in the first image;
communicate the first X-Y voltage pair; receive a second image,
wherein the second image provides alignment information of the
objective lens and the second image is a result of settings of the
first X-Y voltage pair; determine a second X-Y voltage pair based
on the second image, wherein the second X-Y voltage pair provides
alignment of the objective lens closer to the center of the
alignment target than the first X-Y voltage pair; and communicate
the second X-Y voltage pair.
12. The non-transitory computer readable medium of claim 11,
wherein the first X-Y voltage pair is one class.
13. The non-transitory computer readable medium of claim 11,
wherein the second X-Y voltage pair is a continuous value.
14. The non-transitory computer readable medium of claim 11,
wherein the second X-Y voltage pair is based on an average of a
plurality of results.
15. The non-transitory computer readable medium of claim 11,
wherein determining the first X-Y voltage pair uses a first deep
learning neural network, and wherein the first deep learning neural
network includes a classification network.
16. The non-transitory computer readable medium of claim 11,
wherein determining the second X-Y voltage pair uses a second deep
learning neural network, and wherein the second deep learning
neural network includes a regression network ensemble.
17. The non-transitory computer readable medium of claim 11,
wherein the first X-Y voltage pair and the second X-Y voltage pair
are communicated to the scanning electron microscope system.
18. A system comprising: a control unit including a processor, a
memory, and a communication port in electronic communication with a
scanning electron microscope system, wherein the control unit is
configured to: receive a first image, wherein the first image
provides alignment information of an objective lens in the scanning
electron microscope; determine a first X-Y voltage pair based on
the first image, wherein the first X-Y voltage pair provides
alignment of the objective lens closer to a center of an alignment
target than in the first image; communicate the first X-Y voltage
pair to the scanning electron microscope system; receive a second
image, wherein the second image provides alignment information of
the objective lens and the second image is a result of settings of
the first X-Y voltage pair; determine a second X-Y voltage pair
based on the second image, wherein the second X-Y voltage pair
provides alignment of the objective lens closer to the center of
the alignment target than the first X-Y voltage pair; and
communicate the second X-Y voltage pair to the scanning electron
microscope system.
19. The system of claim 18, further comprising an electron beam
source, an electron optical column having a Q4 lens and the
objective lens, and a detector, wherein the control unit is in
electronic communication with the Q4 lens and the detector.
20. The system of claim 18, wherein the first image and the second
image are of a carbon substrate with gold-plate tin spheres on a
carbon substrate.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to the provisional patent
application filed Jun. 29, 2017 and assigned U.S. App. No.
62/526,804, the disclosure of which is hereby incorporated by
reference.
FIELD OF THE DISCLOSURE
[0002] This disclosure relates to calibration in electron beam
systems.
BACKGROUND OF THE DISCLOSURE
[0003] Fabricating semiconductor devices, such as logic and memory
devices, typically includes processing a semiconductor wafer using
a large number of semiconductor fabrication processes to form
various features and multiple levels of the semiconductor devices.
For example, lithography is a semiconductor fabrication process
that involves transferring a pattern from a reticle to a
photoresist arranged on a semiconductor wafer. Additional examples
of semiconductor fabrication processes include, but are not limited
to, chemical-mechanical polishing (CMP), etch, deposition, and ion
implantation. Multiple semiconductor devices may be fabricated in
an arrangement on a single semiconductor wafer and then separated
into individual semiconductor devices.
[0004] Inspection processes are used at various steps during
semiconductor manufacturing to detect defects on wafers to promote
higher yield in the manufacturing process and, thus, higher
profits. Inspection has always been an important part of
fabricating semiconductor devices such as integrated circuits.
However, as the dimensions of semiconductor devices decrease,
inspection becomes even more important to the successful
manufacture of acceptable semiconductor devices because smaller
defects can cause the devices to fail. For instance, as the
dimensions of semiconductor devices decrease, detection of defects
of decreasing size has become necessary since even relatively small
defects may cause unwanted aberrations in the semiconductor
devices.
[0005] A scanning electron microscope (SEM) can be used during
semiconductor manufacturing to detect defects. An SEM system
typically consists of three imaging related subsystems: an electron
source (or electron gun), electron optics (e.g., electrostatic
and/or magnetic lenses), and a detector. Together, these components
form a column of the SEM system. Column calibration may be
performed to ensure proper working condition and good image quality
in the SEM system, which includes aligning various components in
the column against the electron beam. Objective lens alignment
(OLA) is one such calibration task. OLA aligns the electron beam
against the objective lens (OL) by adjusting the beam alignment to
make sure the beam passes through the center of OL. In the SEM
system 100 of FIG. 1, an electron source 101 generates an electron
beam 102 (shown with a dotted line), which passes through the Q4
lens 103 and objective lens 104 toward the wafer 105 on the platen
106.
[0006] OLA typically uses a calibration chip mounted on a stage as
an alignment target. An aligned OL with a target image that is
centered in an image field of view (FOV) is shown in image 200
(with corresponding electron beam position) of FIG. 2. If the OL is
not aligned, the beam center will be off from the lens center. As
such, the target image will appear off centered in the image FOV,
as seen in image 201 (with corresponding electron beam position) of
FIG. 2. The amount of image offset is directly proportional to the
amount of misalignment. By detecting the offsets in X and Y and
converting them to the X and Y voltages (Vx, Vy) applied to beam
aligner, the target image can be brought back to the center, which
can provide aligned OL.
[0007] The current OLA is an iterative procedure that uses multiple
progressively-smaller FOVs. First, control software sets the FOV to
a certain value and adjusts beam aligner voltages while wobbling
wafer bias, which causes target patterns in FOV to shift laterally
if the OL is not aligned. A pattern matching algorithm detects the
shift. The shift in pixels is converted to voltages using a lookup
table and sent to the beam aligner to minimize the shift. Then the
FOV is lowered to certain smaller value and the same steps are
repeated. The FOV is then further lowered to even smaller value and
the final round of adjustment is performed to make the image
steady, which completes the alignment.
[0008] This technique has multiple disadvantages. The beam aligner
adjustment needs to happen using at least three different FOVs and
multiple images must be acquired at each FOV. This is a slow,
tedious process because there are many electron beams and each
electron beam must be aligned periodically. Furthermore, for higher
alignment accuracy, the FOV needs to go below 3 .mu.m. However, the
smallest target on a calibration chip is about 0.5 .mu.m. When FOV
goes below 3 .mu.m, the target image becomes too large. In
addition, due to the special scanning setup for OLA, only a small
area (e.g., approximately 0.2 to 0.3 .mu.m) around the beam center
is in focus, which further reduces the effective FOV. As a result,
the target may fall outside the focus area and become invisible.
This can make alignment fail and limit the achievable alignment
accuracy.
[0009] Therefore, an improved technique and system for calibration
is needed.
BRIEF SUMMARY OF THE DISCLOSURE
[0010] In a first embodiment, a method is provided. A first image
is received at a control unit. The first image provides alignment
information of an objective lens in a scanning electron microscope
system. Using the control unit, a first X-Y voltage pair is
determined based on the first image. The first X-Y voltage pair
provides alignment of the objective lens closer to a center of an
alignment target than in the first image. The first X-Y voltage
pair is communicated to the scanning electron microscope system
using the control unit. A second image is received at the control
unit. The second image provides alignment information of the
objective lens and the second image is a result of settings of the
first X-Y voltage pair. Using the control unit, a second X-Y
voltage pair is determined based on the second image. The second
X-Y voltage pair provides alignment of the objective lens closer to
the center of the alignment target than the first X-Y voltage pair.
The second X-Y voltage pair is communicated to the scanning
electron microscope system using the control unit.
[0011] The first X-Y voltage pair may be one class.
[0012] The second X-Y voltage pair may be a continuous value.
[0013] The second X-Y voltage pair can be based on an average of a
plurality of results.
[0014] The method can further include applying the first X-Y
voltage pair to a Q4 lens of the scanning electron microscope
before generating the second image and applying the second X-Y
voltage pair to the Q4 lens of the scanning electron
microscope.
[0015] Determining the first X-Y voltage pair can use a first deep
learning neural network. The first deep learning neural network can
include a classification network.
[0016] Determining the second X-Y voltage pair can use a second
deep learning neural network. The second deep learning neural
network can include a regression network ensemble.
[0017] The first image and the second image can be of a carbon
substrate with gold-plated tin spheres on a carbon substrate.
[0018] In a second embodiment, a non-transitory computer readable
medium storing a program is provided. The program is configured to
instruct a processor to: receive a first image, wherein the first
image provides alignment information of an objective lens in a
scanning electron microscope system; determine a first X-Y voltage
pair based on the first image, wherein the first X-Y voltage pair
provides alignment of the objective lens closer to a center of an
alignment target than in the first image; communicate the first X-Y
voltage pair; receive a second image, wherein the second image
provides alignment information of the objective lens and the second
image is a result of settings of the first X-Y voltage pair;
determine a second X-Y voltage pair based on the second image,
wherein the second X-Y voltage pair provides alignment of the
objective lens closer to the center of the alignment target than
the first X-Y voltage pair; and communicate the second X-Y voltage
pair.
[0019] The first X-Y voltage pair may be one class.
[0020] The second X-Y voltage pair may be a continuous value.
[0021] The second X-Y voltage pair can be based on an average of a
plurality of results.
[0022] Determining the first X-Y voltage pair can use a first deep
learning neural network that includes a classification network.
[0023] Determining the second X-Y voltage pair can use a second
deep learning neural network that includes a regression network
ensemble.
[0024] The first X-Y voltage pair and the second X-Y voltage pair
can be communicated to the scanning electron microscope system.
[0025] In a third embodiment, a system is provided. The system
comprises a control unit. The control unit includes a processor, a
memory, and a communication port in electronic communication with a
scanning electron microscope system. The control unit is configured
to: receive a first image, wherein the first image provides
alignment information of an objective lens in the scanning electron
microscope; determine a first X-Y voltage pair based on the first
image, wherein the first X-Y voltage pair provides alignment of the
objective lens closer to a center of an alignment target than in
the first image; communicate the first X-Y voltage pair to the
scanning electron microscope system; receive a second image,
wherein the second image provides alignment information of the
objective lens and the second image is a result of settings of the
first X-Y voltage pair; determine a second X-Y voltage pair based
on the second image, wherein the second X-Y voltage pair provides
alignment of the objective lens closer to the center of the
alignment target than the first X-Y voltage pair; and communicate
the second X-Y voltage pair to the scanning electron microscope
system.
[0026] The system can further include an electron beam source, an
electron optical column having a Q4 lens and the objective lens,
and a detector. The control unit can be in electronic communication
with the Q4 lens and the detector.
[0027] The first image and the second image can be of a carbon
substrate with gold-plated tin spheres on a carbon substrate.
DESCRIPTION OF THE DRAWINGS
[0028] For a fuller understanding of the nature and objects of the
disclosure, reference should be made to the following detailed
description taken in conjunction with the accompanying drawings, in
which:
[0029] FIG. 1 is a block diagram illustrating a column in an
exemplary SEM system during operation;
[0030] FIG. 2 is a top and corresponding side view of a block
diagram including a Q4 lens with both an aligned OL and misaligned
OL;
[0031] FIG. 3 is a view of a resolution standard;
[0032] FIG. 4 is a view of a resolution standard that is centered,
indicating a properly-aligned objective lens;
[0033] FIG. 5 is a view of a resolution standard that is not
centered, indicating a misaligned objective lens;
[0034] FIG. 6 is a flowchart of an alignment embodiment in
accordance with the present disclosure; and
[0035] FIG. 7 is a block diagram of an embodiment of a system in
accordance with the present disclosure.
DETAILED DESCRIPTION OF THE DISCLOSURE
[0036] Although claimed subject matter will be described in terms
of certain embodiments, other embodiments, including embodiments
that do not provide all of the benefits and features set forth
herein, are also within the scope of this disclosure. Various
structural, logical, process step, and electronic changes may be
made without departing from the scope of the disclosure.
Accordingly, the scope of the disclosure is defined only by
reference to the appended claims.
[0037] Embodiments disclosed herein can achieve high sensitivity
for OLA of an SEM system with fewer image acquisitions. The
automatic calibration method is more reliable, achieves higher
alignment accuracy, and reduces calibration time. Thus, a faster
and more accurate estimation of beam alignment is provided.
[0038] FIG. 3 is a view of a resolution standard. A resolution
standard can be used as the alignment target. The resolution
standard may be a carbon substrate with gold-plated tin spheres
randomly deposited on its surface. FIG. 4 is a view of a resolution
standard that is centered, indicating a properly-aligned objective
lens. FIG. 5 is a view of a resolution standard that is not
centered, indicating a misaligned objective lens. Note the
difference in location of the gold-plated tin spheres between FIG.
4 and FIG. 5. The misaligned objective lens is not centered.
[0039] While other targets can be used instead of the resolution
standard with the gold-plated tin spheres, the resolution standard
provides small features. For example, the diameters of the spheres
can be from approximately 20 nm to 100 nm. The spheres can be
distributed in an extended area (e.g., 20 mm.sup.2), so there may
be spheres within the focused area of any FOV. This can provide
faster fine objective lens alignment (FOLA) without losing
accuracy.
[0040] FIG. 6 is a flowchart of a method 300 of alignment. In the
method 300, a first image is received at a control unit at 301. The
first image provides alignment information of an objective lens.
For example, the first image may be the image 200 in FIG. 2 or the
image in FIG. 5.
[0041] Using the control unit, a first X-Y voltage pair is
determined 302 based on the first image. The first X-Y voltage pair
provides better alignment of the objective lens. The alignment may
be closer to a center of an alignment target than in the first
image. The center location (X, Y) of the in-focus area in the first
image corresponds to the X-Y voltages. In other words,
(V.sub.x,V.sub.y)=f(x, y). This relationship can be learned by a
neural network during training. At runtime, the network can receive
the first image and output a corresponding voltage based on (X, Y)
information in the image.
[0042] Determining 302 the first X-Y voltage pair can use a
classification network, which may be a deep learning neural
network. The classification network can bin an input image into one
of many classes, each of which corresponds to one beam aligner
voltage. The classification network can try to minimize the
difference between a ground truth voltage and an estimated voltage.
The classification network will generate corresponding X and Y
voltages based on the image, which can be used to better center the
beam.
[0043] The classification network can learn all the bin voltages if
all images for every possible voltage are used to train the
classification network. However, to reduce the complexity of the
training task, a classification network can be trained to output
the coarse voltage using images acquired at a coarse voltage grid.
At runtime when receiving an image, this classification network can
generate a voltage pair that falls on one of the coarse grid points
whose images were used for training. Since the runtime image can
come from voltages between coarse grid points, but the
classification network output is the closest coarse grid point, the
accuracy of classification network may be half of the coarse grid
spacing.
[0044] Another option to generate the coarse voltage is to use an
iterative procedure that uses multiple progressively-smaller
FOVs.
[0045] Using the control unit, the first X-Y voltage pair is
communicated 303, such as to an SEM system that can apply the first
X-Y voltage pair in the Q4 lens or other optical components.
[0046] A second image is received at the control unit at 304. The
second image provides alignment information of the objective lens
and the second image is a result of settings of the first X-Y
voltage pair. Thus, the settings are changed to the first X-Y
voltage pair and the second image is obtained.
[0047] Using the control unit, a second X-Y voltage pair is
determined 305 based on the second image. The second X-Y voltage
pair provides alignment of the objective lens closer to the center
of the alignment target than the first X-Y voltage pair. This
second X-Y voltage pair may be determined using the same approach
as is used to determine the first X-Y voltage pair or a different
approach.
[0048] Determining 305 the second X-Y voltage pair can use a
regression network ensemble, which may be a deep learning neural
network. With the ensemble of multiple regression networks, each
regression network can take an input image and generate one X-Y
voltage pair within certain range on each axis.
[0049] The regression network may be similar to the classification
network. One difference is the last layer of the network. Whereas a
regression network generates a continuous output, a classification
network uses a softmax layer that generates multiple outputs
representing the probability of the input belonging to a particular
class. Another difference is the cost function used for training.
The regression network tends to use L2 norm or some kind of
distance measure between the ground truth value and the network
output value as the cost function, while a classification network
usually uses log likelihood as the cost function.
[0050] In an instance, the second X-Y voltage pair is based on an
average of a plurality of results. For example, multiple regression
networks can each provide an X-Y voltage pair and the resulting X-Y
voltage pairs are averaged to produce the second X-Y voltage
pair.
[0051] Using the control unit, the second X-Y voltage pair is
communicated 306, such as to an SEM system that can apply the
second X-Y voltage pair in the Q4 lens or other optical
components.
[0052] In an instance, the first X-Y voltage pair is one class and
the second X-Y voltage pair is a continuous value.
[0053] Thus, in an instance, a classification network can be used
to find the first X-Y voltage pair and the regression network can
be used to find the second X-Y voltage pair.
[0054] Embodiments of the present disclosure can use deep learning
neural networks to align optical components in an SEM system. The
deep learning based method can directly relate the image to the
voltage, eliminating the need for a lookup table which could bring
additional error if not generated properly. Thus, the images
themselves can be used to determine voltage settings.
[0055] The first image and the second image can be of a carbon
substrate with gold-plated tin spheres on a carbon substrate or
some other substrate.
[0056] Steps 301-303 can be referred to as a coarse process. Steps
304-306 can be referred to as a fine process. An advantage of the
coarse process is that it narrows the computation time needed to
perform the fine process.
[0057] Instead of using template matching with multiple images, the
method 300 can use a deep learning-based algorithm that estimates
the beam aligner voltages with higher accuracy directly from a
single resolution standard image. The coarse-to-fine approach also
can reduce the amount of training images needed to cover an entire
beam aligner X-Y voltage space at certain spacing. Without the
coarse step, there may be too many beam aligner points (e.g.,
images) that the regression network would need to learn. With the
coarse-to-fine approach, the total number beam aligner points to
learn for classification and regression networks together is
reduced.
[0058] Another benefit of the classifier is that a confidence score
associated with each class label output can be provided, which can
be used to filter out bad sites or blurry images. The confidence
scores generated by the classification network is the probability
that an input image belongs to a particular voltage grid point (or
class). The network outputs N confidence scores (N classes) for
each input image. The class of the highest score is assigned to the
input image, which assigns the corresponding voltage to the image
as well. A low confidence score can mean the network is not sure
which voltage grid point it should assign the input image to. This
can happen if the image is acquired from an area on the resolution
standard where tin spheres are missing or damaged, in which case
the low confidence score can tell system to skip the area and move
to another area to grab a new image.
[0059] The classification network and the regression network (or
each of the regression networks) can be trained. An X-Y voltage
pair is applied and a resulting image is obtained. The X voltage
and Y voltages are varied in multiple X-Y voltage pairs and the
process is repeated. These images are each associated with a
particular X-Y voltage pair and can be used to train the
algorithm.
[0060] In addition to X-Y voltage, the focus can be varied such
that the images acquired for training may include images that are
less sharp. This can train the network to work with images that are
not in perfect focus.
[0061] The embodiments described herein may include or be performed
in a system, such as the system 400 of FIG. 7. The system 400
includes an output acquisition subsystem that includes at least an
energy source and a detector. The output acquisition subsystem may
be an electron beam-based output acquisition subsystem. For
example, in one embodiment, the energy directed to the wafer 404
includes electrons, and the energy detected from the wafer 404
includes electrons. In this manner, the energy source may be an
electron beam source 402. In one such embodiment shown in FIG. 7,
the output acquisition subsystem includes electron optical column
401, which is coupled to control unit 407. The control unit 407 can
include one or more processors 408 and one or more memory 409. Each
processor 408 may be in electronic communication with one or more
of the memory 409. In an embodiment, the one or more processors 408
are communicatively coupled. In this regard, the one or more
processors 408 may receive the image of the wafer 404 and store the
image in the memory 409 of the control unit 407. The control unit
407 also may include a communication port 410 in electronic
communication with at least one processor 408. The control unit 407
may be part of an SEM itself or may be separate from the SEM (e.g.,
a standalone control unit or in a centralized quality control
unit).
[0062] As also shown in FIG. 7, the electron optical column 401
includes electron beam source 402 configured to generate electrons
that are focused to the wafer 404 by one or more elements 403. The
electron beam source 402 may include an emitter and the one or more
elements 403 may include, for example, a gun lens, an anode, a beam
limiting aperture, a gate valve, a beam current selection aperture,
an objective lens, a Q4 lens, and/or a scanning subsystem. The
electron column 401 may include any other suitable elements known
in the art. While only one electron beam source 402 is illustrated,
the system 400 may include multiple electron beam sources 402.
[0063] Electrons returned from the wafer 404 (e.g., secondary
electrons) may be focused by one or more elements 405 to the
detector 406. One or more elements 405 may include, for example, a
scanning subsystem, which may be the same scanning subsystem
included in element(s) 403. The electron column 401 may include any
other suitable elements known in the art.
[0064] Although the electron column 401 is shown in FIG. 7 as being
configured such that the electrons are directed to the wafer 404 at
an oblique angle of incidence and are scattered from the wafer at
another oblique angle, it is to be understood that the electron
beam may be directed to and scattered from the wafer at any
suitable angle. In addition, the electron beam-based output
acquisition subsystem may be configured to use multiple modes to
generate images of the wafer 404 (e.g., with different illumination
angles, collection angles, etc.). The multiple modes of the
electron beam-based output acquisition subsystem may be different
in any image generation parameters of the output acquisition
subsystem.
[0065] The control unit 407 may be in electronic communication with
the detector 406 or other components of the system 400. The
detector 406 may detect electrons returned from the surface of the
wafer 404 thereby forming electron beam images of the wafer 404.
The electron beam images may include any suitable electron beam
images. The control unit 407 may be configured according to any of
the embodiments described herein. The control unit 407 also may be
configured to perform other functions or additional steps using the
output of the detector 406 and/or the electron beam images. For
example, the control unit 407 may be programmed to perform some or
all of the steps of FIG. 6.
[0066] It is to be appreciated that the control unit 407 may be
implemented in practice by any combination of hardware, software,
and firmware. Also, its functions as described herein may be
performed by one unit, or divided up among different components,
each of which may be implemented in turn by any combination of
hardware, software, and firmware. Program code or instructions for
the control unit 407 to implement various methods and functions may
be stored in controller readable storage media, such as a memory
409, within the control unit 407, external to the control unit 407,
or combinations thereof.
[0067] It is noted that FIG. 7 is provided herein to generally
illustrate a configuration of an electron beam-based output
acquisition subsystem. The electron beam-based output acquisition
subsystem configuration described herein may be altered to optimize
the performance of the output acquisition subsystem as is normally
performed when designing a commercial output acquisition system. In
addition, the system described herein or components thereof may be
implemented using an existing system (e.g., by adding functionality
described herein to an existing system). For some such systems, the
methods described herein may be provided as optional functionality
of the system (e.g., in addition to other functionality of the
system).
[0068] While disclosed as part of a defect review system, the
control unit 407 or methods described herein may be configured for
use with inspection systems. In another embodiment, the control
unit 407 or methods described herein may be configured for use with
a metrology system. Thus, the embodiments as disclosed herein
describe some configurations for classification that can be
tailored in a number of manners for systems having different
imaging capabilities that are more or less suitable for different
applications.
[0069] In particular, the embodiments described herein may be
installed on a computer node or computer cluster that is a
component of or coupled to the detector 406 or another component of
a defect review tool, a mask inspector, a virtual inspector, or
other devices. In this manner, the embodiments described herein may
generate output that can be used for a variety of applications that
include, but are not limited to, wafer inspection, mask inspection,
electron beam inspection and review, metrology, or other
applications. The characteristics of the system 400 shown in FIG. 7
can be modified as described above based on the specimen for which
it will generate output.
[0070] The control unit 407, other system(s), or other subsystem(s)
described herein may take various forms, including a personal
computer system, workstation, image computer, mainframe computer
system, workstation, network appliance, internet appliance,
parallel processor, or other device. In general, the term "control
unit" may be broadly defined to encompass any device having one or
more processors that executes instructions from a memory medium.
The subsystem(s) or system(s) may also include any suitable
processor known in the art, such as a parallel processor. In
addition, the subsystem(s) or system(s) may include a platform with
high speed processing and software, either as a standalone or a
networked tool.
[0071] If the system includes more than one subsystem, then the
different subsystems may be coupled to each other such that images,
data, information, instructions, etc. can be sent between the
subsystems. For example, one subsystem may be coupled to additional
subsystem(s) by any suitable transmission media, which may include
any suitable wired and/or wireless transmission media known in the
art. Two or more of such subsystems may also be effectively coupled
by a shared computer-readable storage medium (not shown).
[0072] In another embodiment, the control unit 407 may be
communicatively coupled to any of the various components or
sub-systems of system 400 in any manner known in the art. Moreover,
the control unit 407 may be configured to receive and/or acquire
data or information from other systems (e.g., inspection results
from an inspection system such as a broad band plasma (BBP) tool, a
remote database including design data and the like) by a
transmission medium that may include wired and/or wireless
portions. In this manner, the transmission medium may serve as a
data link between the control unit 407 and other subsystems of the
system 400 or systems external to system 400.
[0073] The control unit 407 may be coupled to the components of the
system 400 in any suitable manner (e.g., via one or more
transmission media, which may include wired and/or wireless
transmission media) such that the control unit 407 can receive the
output generated by the system 400. The control unit 407 may be
configured to perform a number of functions using the output. In
another example, the control unit 407 may be configured to send the
output to a memory 409 or another storage medium without performing
defect review on the output. The control unit 407 may be further
configured as described herein.
[0074] An additional embodiment relates to a non-transitory
computer-readable medium storing program instructions executable on
a controller for performing a computer-implemented method for
aligning an SEM system, as disclosed herein. In particular, as
shown in FIG. 7, the control unit 407 can include a memory 409 or
other electronic data storage medium with non-transitory
computer-readable medium that includes program instructions
executable on the control unit 407. The computer-implemented method
may include any step(s) of any method(s) described herein. The
memory 409 or other electronic data storage medium may be a storage
medium such as a magnetic or optical disk, a magnetic tape, or any
other suitable non-transitory computer-readable medium known in the
art.
[0075] The program instructions may be implemented in any of
various ways, including procedure-based techniques, component-based
techniques, and/or object-oriented techniques, among others. For
example, the program instructions may be implemented using ActiveX
controls, C++ objects, JavaBeans, Microsoft Foundation Classes
(MFC), SSE (Streaming SIMD Extension), or other technologies or
methodologies, as desired.
[0076] In some embodiments, various steps, functions, and/or
operations of system 400 and the methods disclosed herein are
carried out by one or more of the following: electronic circuits,
logic gates, multiplexers, programmable logic devices, ASICs,
analog or digital controls/switches, microcontrollers, or computing
systems. Program instructions implementing methods such as those
described herein may be transmitted over or stored on carrier
medium. The carrier medium may include a storage medium such as a
read-only memory, a random access memory, a magnetic or optical
disk, a non-volatile memory, a solid state memory, a magnetic tape
and the like. A carrier medium may include a transmission medium
such as a wire, cable, or wireless transmission link. For instance,
the various steps described throughout the present disclosure may
be carried out by a single control unit 407 (or computer system)
or, alternatively, multiple control units 407 (or multiple computer
systems). Moreover, different sub-systems of the system 400 may
include one or more computing or logic systems. Therefore, the
above description should not be interpreted as a limitation on the
present invention but merely an illustration.
[0077] Each of the steps of the method may be performed as
described herein. The methods also may include any other step(s)
that can be performed by the control unit and/or computer
subsystem(s) or system(s) described herein. The steps can be
performed by one or more computer systems, which may be configured
according to any of the embodiments described herein. In addition,
the methods described above may be performed by any of the system
embodiments described herein.
[0078] Although the present disclosure has been described with
respect to one or more particular embodiments, it will be
understood that other embodiments of the present disclosure may be
made without departing from the scope of the present disclosure.
Hence, the present disclosure is deemed limited only by the
appended claims and the reasonable interpretation thereof.
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